Thomas Burger

TL;DR: Practical guidelines are formulated regarding the choice and the application of an imputation method in a proteomics context and it is shown that a supposedly "under-performing" method, if applied at the "appropriate" time in the data-processing pipeline (before or after peptide aggregation) on a data set with the 'appropriate' nature of missing values, can outperform a blindly applied, supposedly "better-performing' method.

TL;DR: DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments and contain procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate.

TL;DR: This paper starts by examining consistency and conflict on sets and extracts from this settings basic properties that measures of consistency and Conflict should have, and extends this basic scheme to belief functions in different ways.

TL;DR: The present proteomic relies in the identification of photosynthetic proteins whose differential distribution in the thylakoid subcompartments might explain already observed phenomenon such as LHCII docking and can suggest new molecular actors for photosynthesis-linked activities.

TL;DR: The pRoloc project as mentioned in this paper is a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data, which provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection.